In this paper, we propose an approach to solve an “use-inspired” problem: the management of warehouses containing equipment to be installed at various sites throughout the territory by specialized personnel. The challenges to be addressed include the distribution of the warehouse, so as not to slow down work, but also the fact that the tasks to complete change constantly, requiring the opening, closing, and relocation of the corresponding warehouses.

In this article, we investigate a problem of invoice fraud detection in companies. We propose a method that can be applied to invoices received by a company and is used to check if their ID is consistent with the ID of previous documents emitted by the same company. This is one of several tests applied in the fraud detection pipeline and is used to mitigate the company’s daily fraud attempts. The proposed method is based on the automatic discovery of the ID structure, represented by a regular expression, which is used on newly received documents to validate them. The results show that the method can be successfully used to identify fraud invoices with good accuracy

Food security relies on factors like availability, access, and stability, often assisted by food imports when local production falters. Importantly, these imports stabilize supplies, mitigate shortages and price volatility, and enhance economic stability. Anticipating import requirements is vital for proactive food security planning. In this case study, we employ multiple forecasting models to predict food import for a large number of products from multiple countries. The results highlight varying algorithm performance across datasets. Traditional statistical models remain highly competitive compared to newer alternatives, especially for shorter time series. Our study introduces a multi-model forecasting approach to predict periodic food imports, a pivotal tool for food authorities.